An Outline of a Simple, Interpretable Epigenetic Composite Score for Mortality Prediction for Accelerated...

In principle, it is generally accepted that DNA methylation measures can be used to predict mortality. However, as of yet, no epigenetic metric has been successfully incorporated into underwriting procedures. In part, this failure results from the relative incompatibility of many DNA methylation measures with conventional underwriting practices.

To test the ability of previously established epigenetic markers of smoking, drinking and diabetes to standard lipid-based approaches for predicting mortality.

We constructed a series of Cox proportional hazards models for mortality using clinical data and DNA methylation data from 4 previously described loci from the Framingham Heart Study.

The incorporation of vital signs, standard lipid and diabetes laboratory assessments to a base model consisting of age and sex only modestly increased prediction of mortality from 0.732 to 0.741 area under the curve (AUC). However, the addition of epigenetic marker information for smoking and drinking to the base model markedly increased prediction (AUC=0.787) while the addition of epigenetic marker for diabetes increased prediction even further (AUC=0.792).

These results demonstrate the potential of simple interpretable, epigenetic models to predict mortality in a manner compatible with standard underwriting procedures. Potentially, this epigenetic approach using rapid methylation sensitive digital PCR procedures that can utilize saliva or whole blood DNA would increase prediction power even further while facilitating more accurate accelerated underwriting assessments of mortality.